"""simple docstring"""


import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Tuple:




						'''simple docstring'''



						lowerCamelCase__   =torch.exp(__lowerCAmelCase							)
						lowerCamelCase__   =torch.sum(__lowerCAmelCase  ,       dim=1							)  # sum of exp(x_i)
						lowerCamelCase__   =torch.sum(x * exp_x  ,       dim=1							)  # sum of x_i * exp(x_i)
						return torch.log(__lowerCAmelCase							) - B / A






class   __UpperCAmelCase		(      nn.Module      ):

				def __init__( self      ,					_lowerCamelCase ):
										super().__init__()
										lowerCamelCase__   =config.output_attentions
										lowerCamelCase__   =config.output_hidden_states
										lowerCamelCase__   =nn.ModuleList([BertLayer(_lowerCamelCase ) for _ in range(config.num_hidden_layers )] )
										lowerCamelCase__   =nn.ModuleList([BertHighway(_lowerCamelCase ) for _ in range(config.num_hidden_layers )] )

										lowerCamelCase__   =[-1 for _ in range(config.num_hidden_layers )]

				def 						_a      ( self      ,					_lowerCamelCase ):
										if (type(_lowerCamelCase ) is float) or (type(_lowerCamelCase ) is int):
																for i in range(len(self.early_exit_entropy ) ):
																						lowerCamelCase__   =x
										else:
																lowerCamelCase__   =x

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =pooler.state_dict()
										for highway in self.highway:
																for name, param in highway.pooler.state_dict().items():
																						param.copy_(loaded_model[name] )






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					):
										lowerCamelCase__   =()
										lowerCamelCase__   =()
										lowerCamelCase__   =()
										for i, layer_module in enumerate(self.layer ):
																if self.output_hidden_states:
																						lowerCamelCase__   =all_hidden_states + (hidden_states,)

																lowerCamelCase__   =layer_module(
																    _lowerCamelCase      ,					_lowerCamelCase      ,					head_mask[i]      ,					_lowerCamelCase      ,					_lowerCamelCase )
																lowerCamelCase__   =layer_outputs[0]

																if self.output_attentions:
																						lowerCamelCase__   =all_attentions + (layer_outputs[1],)

																lowerCamelCase__   =(hidden_states,)
																if self.output_hidden_states:
																						lowerCamelCase__   =current_outputs + (all_hidden_states,)
																if self.output_attentions:
																						lowerCamelCase__   =current_outputs + (all_attentions,)

																lowerCamelCase__   =self.highway[i](_lowerCamelCase )
																# logits, pooled_output

																if not self.training:
																						lowerCamelCase__   =highway_exit[0]
																						lowerCamelCase__   =entropy(_lowerCamelCase )
																						lowerCamelCase__   =highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																						lowerCamelCase__   =all_highway_exits + (highway_exit,)

																						if highway_entropy < self.early_exit_entropy[i]:
																												lowerCamelCase__   =(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																												raise HighwayException(_lowerCamelCase      ,					i + 1 )
																else:
																						lowerCamelCase__   =all_highway_exits + (highway_exit,)

        # Add last layer
										if self.output_hidden_states:
																lowerCamelCase__   =all_hidden_states + (hidden_states,)

										lowerCamelCase__   =(hidden_states,)
										if self.output_hidden_states:
																lowerCamelCase__   =outputs + (all_hidden_states,)
										if self.output_attentions:
																lowerCamelCase__   =outputs + (all_attentions,)

										lowerCamelCase__   =outputs + (all_highway_exits,)
										return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). ''' , __lowerCAmelCase , )
class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				def __init__( self      ,					_lowerCamelCase ):
										super().__init__(_lowerCamelCase )
										lowerCamelCase__   =config

										lowerCamelCase__   =BertEmbeddings(_lowerCamelCase )
										lowerCamelCase__   =DeeBertEncoder(_lowerCamelCase )
										lowerCamelCase__   =BertPooler(_lowerCamelCase )

										self.init_weights()

				def 						_a      ( self ):
										self.encoder.init_highway_pooler(self.pooler )

				def 						_a      ( self ):
										return self.embeddings.word_embeddings

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =value

				def 						_a      ( self      ,					_lowerCamelCase ):
										for layer, heads in heads_to_prune.items():
																self.encoder.layer[layer].attention.prune_heads(_lowerCamelCase )






				@add_start_docstrings_to_model_forward(_lowerCamelCase )
				def 						_a      ( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					):
										if input_ids is not None and inputs_embeds is not None:
																raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time" )
										elif input_ids is not None:
																lowerCamelCase__   =input_ids.size()
										elif inputs_embeds is not None:
																lowerCamelCase__   =inputs_embeds.size()[:-1]
										else:
																raise ValueError("You have to specify either input_ids or inputs_embeds" )

										lowerCamelCase__   =input_ids.device if input_ids is not None else inputs_embeds.device

										if attention_mask is None:
																lowerCamelCase__   =torch.ones(_lowerCamelCase      ,					device=_lowerCamelCase )
										if encoder_attention_mask is None:
																lowerCamelCase__   =torch.ones(_lowerCamelCase      ,					device=_lowerCamelCase )
										if token_type_ids is None:
																lowerCamelCase__   =torch.zeros(_lowerCamelCase      ,					dtype=torch.long      ,					device=_lowerCamelCase )

										# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
										# ourselves in which case we just need to make it broadcastable to all heads.
										lowerCamelCase__   =self.get_extended_attention_mask(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )

										# If a 2D ou 3D attention mask is provided for the cross-attention
										# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
										if encoder_attention_mask.dim() == 3:
																lowerCamelCase__   =encoder_attention_mask[:, None, :, :]
										if encoder_attention_mask.dim() == 2:
																lowerCamelCase__   =encoder_attention_mask[:, None, None, :]

										lowerCamelCase__   =encoder_extended_attention_mask.to(
										    dtype=next(self.parameters() ).dtype )  # fp16 compatibility
										lowerCamelCase__   =(1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

										# Prepare head mask if needed
										# 1.0 in head_mask indicate we keep the head
										# attention_probs has shape bsz x n_heads x N x N
										# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
										# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
										lowerCamelCase__   =self.get_head_mask(_lowerCamelCase      ,					self.config.num_hidden_layers )

										lowerCamelCase__   =self.embeddings(
										    input_ids=_lowerCamelCase      ,					position_ids=_lowerCamelCase      ,					token_type_ids=_lowerCamelCase      ,					inputs_embeds=_lowerCamelCase )
										lowerCamelCase__   =self.encoder(
										    _lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					head_mask=_lowerCamelCase      ,					encoder_hidden_states=_lowerCamelCase      ,					encoder_attention_mask=_lowerCamelCase      ,					)
										lowerCamelCase__   =encoder_outputs[0]
										lowerCamelCase__   =self.pooler(_lowerCamelCase )

										lowerCamelCase__   =(
										    sequence_output,
										    pooled_output,
										) + encoder_outputs[
										    1:
										]  # add hidden_states and attentions if they are here
										return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =message
										lowerCamelCase__   =exit_layer  # start from 1!






class   __UpperCAmelCase		(      nn.Module      ):

				def __init__( self      ,					_lowerCamelCase ):
										super().__init__()
										lowerCamelCase__   =BertPooler(_lowerCamelCase )
										lowerCamelCase__   =nn.Dropout(config.hidden_dropout_prob )
										lowerCamelCase__   =nn.Linear(config.hidden_size      ,					config.num_labels )






				def 						_a      ( self      ,					_lowerCamelCase ):
										# Pooler
										lowerCamelCase__   =encoder_outputs[0]
										lowerCamelCase__   =self.pooler(_lowerCamelCase )
										# "return" pooler_output

										# BertModel
										lowerCamelCase__   =(pooler_input, pooler_output) + encoder_outputs[1:]
										# "return" bmodel_output

										# Dropout and classification
										lowerCamelCase__   =bmodel_output[1]

										lowerCamelCase__   =self.dropout(_lowerCamelCase )
										lowerCamelCase__   =self.classifier(_lowerCamelCase )

										return logits, pooled_output






@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. ''' , __lowerCAmelCase , )
class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				def __init__( self      ,					_lowerCamelCase ):
										super().__init__(_lowerCamelCase )
										lowerCamelCase__   =config.num_labels
										lowerCamelCase__   =config.num_hidden_layers

										lowerCamelCase__   =DeeBertModel(_lowerCamelCase )
										lowerCamelCase__   =nn.Dropout(config.hidden_dropout_prob )
										lowerCamelCase__   =nn.Linear(config.hidden_size      ,					self.config.num_labels )

										self.init_weights()






				@add_start_docstrings_to_model_forward(_lowerCamelCase )
				def 						_a      ( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=-1      ,					_lowerCamelCase=False      ,					):
										lowerCamelCase__   =self.num_layers
										try:
																lowerCamelCase__   =self.bert(
																    _lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					token_type_ids=_lowerCamelCase      ,					position_ids=_lowerCamelCase      ,					head_mask=_lowerCamelCase      ,					inputs_embeds=_lowerCamelCase      ,					)
																# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

																lowerCamelCase__   =outputs[1]

																lowerCamelCase__   =self.dropout(_lowerCamelCase )
																lowerCamelCase__   =self.classifier(_lowerCamelCase )
																lowerCamelCase__   =(logits,) + outputs[2:]  # add hidden states and attention if they are here
										except HighwayException as e:
																lowerCamelCase__   =e.message
																lowerCamelCase__   =e.exit_layer
																lowerCamelCase__   =outputs[0]

										if not self.training:
																lowerCamelCase__   =entropy(_lowerCamelCase )
																lowerCamelCase__   =[]
																lowerCamelCase__   =[]
										if labels is not None:
																if self.num_labels == 1:
																						#  We are doing regression
																						lowerCamelCase__   =MSELoss()
																						lowerCamelCase__   =loss_fct(logits.view(-1 )      ,					labels.view(-1 ) )
																else:
																						lowerCamelCase__   =CrossEntropyLoss()
																						lowerCamelCase__   =loss_fct(logits.view(-1      ,					self.num_labels )      ,					labels.view(-1 ) )

																# work with highway exits
																lowerCamelCase__   =[]
																for highway_exit in outputs[-1]:
																						lowerCamelCase__   =highway_exit[0]
																						if not self.training:
																												highway_logits_all.append(_lowerCamelCase )
																												highway_entropy.append(highway_exit[2] )
																						if self.num_labels == 1:
																												#  We are doing regression
																												lowerCamelCase__   =MSELoss()
																												lowerCamelCase__   =loss_fct(highway_logits.view(-1 )      ,					labels.view(-1 ) )
																						else:
																												lowerCamelCase__   =CrossEntropyLoss()
																												lowerCamelCase__   =loss_fct(highway_logits.view(-1      ,					self.num_labels )      ,					labels.view(-1 ) )
																						highway_losses.append(_lowerCamelCase )

																if train_highway:
																						lowerCamelCase__   =(sum(highway_losses[:-1] ),) + outputs
																						# exclude the final highway, of course
																else:
																						lowerCamelCase__   =(loss,) + outputs
										if not self.training:
																lowerCamelCase__   =outputs + ((original_entropy, highway_entropy), exit_layer)
																if output_layer >= 0:
																						lowerCamelCase__   =(
																						    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																						)  # use the highway of the last layer

										return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)



"""simple docstring"""


from .imports import is_rich_available


if is_rich_available():
	from rich.traceback import install

	install(show_locals=False)

else:
	raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')


